# -*- coding:utf-8 -*-

"""
@author: fanqc
@file: image_similarity.py
@time: 2023/2/25 17:25
"""
from io import BytesIO

import cv2
import matplotlib
import numpy as np
import requests
from PIL import Image

matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import urllib3

# 去除请求https安全警告
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)


def averageHash(img):
    """
    均值哈希算法
    """
    # 缩放为8*8
    img = cv2.resize(img, (8, 8))
    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # s为像素和初值为0，hash_str为hash值初值为''
    s = 0
    hash_str = ''
    # 遍历累加求像素和
    for i in range(8):
        for j in range(8):
            s = s + gray[i, j]
    # 求平均灰度
    avg = s / 64
    # 灰度大于平均值为1相反为0生成图片的hash值
    for i in range(8):
        for j in range(8):
            if gray[i, j] > avg:
                hash_str = hash_str + '1'
            else:
                hash_str = hash_str + '0'
    return hash_str


def differentialHash(img):
    """
    差值哈希算法
    """
    # 缩放8*8
    img = cv2.resize(img, (9, 8))
    # 转换灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    hash_str = ''
    # 每行前一个像素大于后一个像素为1，相反为0，生成哈希
    for i in range(8):
        for j in range(8):
            if gray[i, j] > gray[i, j + 1]:
                hash_str = hash_str + '1'
            else:
                hash_str = hash_str + '0'
    return hash_str


def perceptualHash(img):
    """
    感知哈希算法
    """
    # 缩放32*32
    img = cv2.resize(img, (32, 32))  # , interpolation=cv2.INTER_CUBIC
    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 将灰度图转为浮点型，再进行dct变换
    dct = cv2.dct(np.float32(gray))
    # opencv实现的掩码操作
    dct_roi = dct[0:8, 0:8]

    hash = []
    avreage = np.mean(dct_roi)
    for i in range(dct_roi.shape[0]):
        for j in range(dct_roi.shape[1]):
            if dct_roi[i, j] > avreage:
                hash.append(1)
            else:
                hash.append(0)
    return hash


def cmpHash(hash1, hash2):
    """
    Hash值对比 返回汉明距离
    """
    # 算法中1和0顺序组合起来的即是图片的指纹hash。顺序不固定，但是比较的时候必须是相同的顺序。
    # 对比两幅图的指纹，计算汉明距离，即两个64位的hash值有多少是不一样的，不同的位数越小，图片越相似
    # 汉明距离：一组二进制数据变成另一组数据所需要的步骤，可以衡量两图的差异，汉明距离越小，则相似度越高。汉明距离为0，即两张图片完全一样
    n = 0
    # hash长度不同则返回-1代表传参出错
    if len(hash1) != len(hash2):
        return -1
    # 遍历判断
    for i in range(len(hash1)):
        # 不相等则n计数+1，n最终为相似度
        if hash1[i] != hash2[i]:
            n = n + 1
    return n


def bytes_to_cv2_img(filebytes):
    """
    图片字节流转换为cv image
    """
    img = Image.open(filebytes)
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)


def load_img_to_cv2(url_or_link):
    if url_or_link.startswith("http"):
        # 根据链接下载图片，并转换为opencv格式
        img_bytes = getImgByUrl(url_or_link)
        return bytes_to_cv2_img(img_bytes)
    else:
        # 本地路径图片
        return cv2.imread(url_or_link)


def getImgByUrl(url: str):
    """
    根据url下载图片 返回字节流
    """
    try:
        resp = requests.get(url, verify=False)
        return BytesIO(resp.content)
    except Exception as e:
        raise e


def gray_histogram_similarity(img1, img2):
    # 灰度直方图算法
    # 计算单通道的直方图的相似值
    hist1 = cv2.calcHist([img1], [0], None, [256], [0.0, 255.0])
    hist2 = cv2.calcHist([img2], [0], None, [256], [0.0, 255.0])
    # 计算直方图的重合度
    degree = 0
    for i in range(len(hist1)):
        if hist1[i] != hist2[i]:
            degree = degree + \
                     (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i]))
        else:
            degree = degree + 1
    degree = degree / len(hist1)
    return degree


def color_histogram_similarity(img1, img2, size=(256, 256)):
    # RGB每个通道的直方图相似度
    # 将图像resize后，分离为RGB三个通道，再计算每个通道的相似值
    img1 = cv2.resize(img1, size)
    img2 = cv2.resize(img2, size)
    sub_image1 = cv2.split(img1)
    sub_image2 = cv2.split(img2)
    sub_data = 0
    for im1, im2 in zip(sub_image1, sub_image2):
        sub_data += gray_histogram_similarity(im1, im2)
    sub_data = sub_data / 3
    return sub_data


def avg_hash_similarity(img1, img2):
    img1 = load_img_to_cv2(img1)
    img2 = load_img_to_cv2(img2)
    return round(1.0 - (cmpHash(averageHash(img1), averageHash(img2)) / 64), 2)


def dif_hash_similarity(img1, img2):
    img1 = load_img_to_cv2(img1)
    img2 = load_img_to_cv2(img2)
    return round(1.0 - (cmpHash(differentialHash(img1), differentialHash(img2)) / 64), 2)


def per_hash_similarity(img1, img2):
    img1 = load_img_to_cv2(img1)
    img2 = load_img_to_cv2(img2)
    return round(1.0 - (cmpHash(perceptualHash(img1), perceptualHash(img2)) / 64), 2)


def _debug_test(para1, para2):
    # 均值、差值、感知哈希算法三种算法值越小，则越相似,相同图片值为0
    # 三直方图算法和单通道的直方图 0-1之间，值越大，越相似。 相同图片为1

    # t1,t2   14;19;10;  0.70;0.75
    # t1,t3   39 33 18   0.58 0.49
    # s1,s2  7 23 11     0.83 0.86  挺相似的图片
    # c1,c2  11 29 17    0.30 0.31

    img1 = load_img_to_cv2(para1)
    img2 = load_img_to_cv2(para2)

    hash1 = averageHash(img1)
    hash2 = averageHash(img2)
    n1 = cmpHash(hash1, hash2)
    print('均值哈希算法相似度aHash：', n1)

    hash1 = differentialHash(img1)
    hash2 = differentialHash(img2)
    n2 = cmpHash(hash1, hash2)
    print('差值哈希算法相似度dHash：', n2)

    hash1 = perceptualHash(img1)
    hash2 = perceptualHash(img2)
    n3 = cmpHash(hash1, hash2)
    print('感知哈希算法相似度pHash：', n3)

    n4 = color_histogram_similarity(img1, img2)
    print('三直方图算法相似度：', n4[0])

    n5 = gray_histogram_similarity(img1, img2)
    print("单通道的直方图", n5[0])
    print("%d %d %d %.2f %.2f " % (n1, n2, n3, round(n4[0], 2), round(n5[0], 2)))
    print("%.2f %.2f %.2f %.2f %.2f " % (1 - float(n1 / 64), 1 -
                                         float(n2 / 64), 1 - float(n3 / 64), round(n4[0], 2), n5[0]))

    plt.subplot(121)
    plt.imshow(Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)))
    plt.subplot(122)
    plt.imshow(Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)))
    plt.show()


if __name__ == "__main__":
    p1 = "https://img0.baidu.com/it/u=210987429,1967982263&fm=253&fmt=auto&app=138&f=JPEG?w=500&h=333"
    p2 = "https://p0.itc.cn/images01/20201209/1e802f1a82bd42cfa641e49b9e6ff704.jpeg"

    # print(per_hash_similarity(p1, p2))
    print(_debug_test(p1, p2))
